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Detecting when pre-trained nnU-Net models fail silently for Covid-19

2021-07-13 10:48:08
Camila Gonzalez, Karol Gotkowski, Andreas Bucher, Ricarda Fischbach, Isabel Kaltenborn, Anirban Mukhopadhyay

Abstract

Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly.

Abstract (translated)

URL

https://arxiv.org/abs/2107.05975

PDF

https://arxiv.org/pdf/2107.05975.pdf


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